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train.py
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train.py
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# -*- encoding: utf-8 -*-
"""
@File : train.py
@Time : 2020/4/13 17:10
@Author : Alessia K
@Email : ------
"""
import os
from datetime import datetime
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from utils.dataset import SingleDataset
from utils.parse_cfg import load_cfg
import utils.optimizer as optim
import utils.checkpoint as cp
import utils.log_output as log_g
import utils.evalutator as Evaluator
from model.AnyNet import AnyNet
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# initial weights
def init_weights(model, zero_init_gamma=False):
for m in model.modules():
if isinstance(m, torch.nn.Conv2d):
torch.nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, torch.nn.BatchNorm2d):
m.weight.data.fill_(0.0 if zero_init_gamma else 1.0)
torch.nn.init.constant_(m.bias, 0)
elif isinstance(m, torch.nn.Linear):
m.weight.data.normal_(mean=0.0, std=0.001)
def main(cfg):
# basic settings
loss_F = torch.nn.CrossEntropyLoss()
gpu_nums = int(cfg['NUM_GPUS'])
if gpu_nums == 0:
use_cuda = False
else:
use_cuda = True
# load model
model = AnyNet(cfg)
if use_cuda:
model = torch.nn.DataParallel(model, device_ids=[0])
model = model.cuda()
# load_dataset
Trainpath = cfg['TRAIN']['PATH']
RESIZE_SIZE = cfg['TRAIN']['IM_SIZE']
train_data = SingleDataset(Trainpath, split='train', resize_size=RESIZE_SIZE)
train_loader= DataLoader(dataset=train_data, batch_size=cfg['TRAIN']['BATCH_SIZE'],
shuffle=True, num_workers=cfg['DATA_LOADER']['NUM_WORKERS'], pin_memory=True)
Testpath = cfg['TEST']['PATH']
RESIZE_SIZE_val = cfg['TEST']['IM_SIZE']
test_data = SingleDataset(Testpath, split='val', resize_size=RESIZE_SIZE_val)
test_loader = DataLoader(dataset=test_data, batch_size=cfg['TEST']['BATCH_SIZE'],
shuffle=False, num_workers=cfg['DATA_LOADER']['NUM_WORKERS'], pin_memory=True)
# optimizer and loss function and evaluator
if cfg['OPTIM']['OPTIMIZER'] == 'Adam':
optimizer = torch.optim.Adam(model.parameters(), lr=cfg['OPTIM']['BASE_LR'], weight_decay=1e-4)
else:
optimizer = torch.optim.SGD(model.parameters(), lr=cfg['OPTIM']['BASE_LR'], momentum=0.9, weight_decay=5e-4)
# load checkpoint or initial weights
start_epoch = 0
if cfg['TRAIN']['RESUME'] is not None:
resume = cfg['TRAIN']['RESUME']
if not os.path.isfile(resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(resume))
checkpoint_epoch = cp.load_checkpoint(resume, gpu_num=gpu_nums, model=model, optimizer=optimizer)
start_epoch = checkpoint_epoch + 1
elif cfg['TRAIN']['WEIGHTS']:
cp.load_checkpoint(cfg['TRAIN']['WEIGHTS'], gpu_nums, model)
else:
init_weights(model, zero_init_gamma=cfg['BN']['ZERO_INIT_FINAL_GAMMA'])
# save training process
log_file = log_g.get_log_filename(os.path.join(cfg['OUT_DIR'], 'log/'))
log = open(log_file, 'w+')
# start training
max_epoch = cfg['OPTIM']['MAX_EPOCH']
batch_size = cfg['TRAIN']['BATCH_SIZE']
eval_period = cfg['TRAIN']['EVAL_PERIOD']
batch_count = 0
total_step = len(train_loader)
num_class = cfg['MODEL']['NUM_CLASSES']
# correct_all = list(0. for i in range(cfg['MODEL']['NUM_CLASSES']))
# total_all = list(0. for i in range(cfg['MODEL']['NUM_CLASSES']))
for epoch in range(start_epoch, max_epoch):
print('**************train --%d-- **************' % (epoch))
log.write('**************train --%d-- **************\n' % (epoch))
# update learning rate
lr = optim.get_epoch_lr(epoch_i=epoch, cfg=cfg)
optim.set_lr(optimizer, lr)
#############################################################################
# start training an epoch
#############################################################################
model.train()
c_train = 0
t_train = 0
for i, (img, lbl) in enumerate(train_loader):
batch_count += 1
# use cuda
if use_cuda:
img, lbl = img.cuda(), lbl.cuda()
# forward
preds = model(img)
loss = loss_F(preds, lbl)
# backward
# optimizer.zero_grad()
loss.backward()
# optimizer.step()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)
if (batch_count % batch_size) == 0:
optimizer.step()
optimizer.zero_grad()
batch_count = 0
_, predicted = preds.max(1)
c_train += predicted.eq(lbl).sum().item()
t_train += lbl.size(0)
# print epoch, step, loss, lr
print('[%s]--train: %d/%d\tstep:%d/%d----lr:%.5f---loss:%.4f---Acc:%.3f' % (
datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
(epoch + 1), max_epoch, (i + 1), total_step, lr, loss.item(), 100*(c_train/t_train)))
log.write('[%s]--train: [%d/%d]\tstep: [%d/%d]\t----lr:%.5f---loss:%.4f---Acc:%.3f\n' %(
datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
(epoch + 1), max_epoch, (i + 1), total_step, lr, loss.item(), 100*(c_train/t_train)))
#############################################################################
# start validation
#############################################################################
if ((epoch+1) % eval_period == 0):
print('**************validation --%d-- **************' % ((epoch + 1) // eval_period))
model.eval()
mean_loss_val = 0
correct = np.zeros((num_class))
total = np.zeros((num_class))
top1_acc_sum = []
with torch.no_grad():
for val_epoch, (img_val, lbl_val) in enumerate(test_loader):
if use_cuda:
img_val, lbl_val = img_val.cuda(), lbl_val.cuda()
# predict
preds_val = model(img_val)
# calculate loss
loss_val = loss_F(preds_val, lbl_val)
mean_loss_val += loss_val.item()
# evaluation
top1_acc, top2_acc = Evaluator.accuracy(preds_val, lbl_val, [1,2])
correct_i, total_i = Evaluator.accuracy_perclass(preds_val, lbl_val, num_class)
correct += correct_i
total += total_i
top1_acc_sum.append(top1_acc)
print('[%s]--valid: [%d/%d]\tloss: %.4f---top1_acc: %.3f' % (
datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
val_epoch, len(test_loader), loss_val.item(), top1_acc.item()))
print('[{}]--valid: [{}]\tmean_loss: {}\ttop1_acc: {}\tper_class_acc: {}'.format(
datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
(epoch + 1), (mean_loss_val / len(test_loader)), np.mean(top1_acc_sum), 100*(correct/total)))
# save log
log.write('[{}]--valid: [{}]\tmean_loss: {}\ttop1_acc: {}\tper_class_acc: {}\n'.format(
datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
(epoch + 1), (mean_loss_val / val_epoch), np.mean(top1_acc_sum), 100*(correct/total)))
#############################################################################
# save model
#############################################################################
if ((epoch+1)%5==0):
checkpoint_file = os.path.join(cfg['OUT_DIR'], 'checkpoint/')
checkpoint_filename = cp.save_checkpoint(model, optimizer, epoch, gpu_nums, checkpoint_file)
log.write('[{}]--save checkpoint: {}\n'.format(
datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
checkpoint_filename
))
log.close()
if __name__ == '__main__':
# basic parmas
cfg = load_cfg('data/AnyNet_cpu.yaml')
main(cfg)